CBAM-YOLOv5: A Promising Network Model for Wear Particle Recognition

Abstract

The intelligent recognition technology for ferrography images is one of the important methods for diagnosis fault and state detection of machines. In allusion to these questions for the influences of wear particle images’ blurring, background intricacy, wear particle overlapping and lack of light, and others which lead to be the reason for the difficulty of achieving accurate identification, missed detection, and false detection, an intelligent recognition algorithm for ferrography wear particle based on convolutional block attention module (CBAM) and YOLOv5 is proposed. Firstly, it needs enhancement to improve contrast for ferrography wear particle images and lower background interference by adaptive histogram homogenization algorithm. Then, under the framework of YOLOv5 algorithm, the depthwise separable convolution is added to improve the detection speed of the network, and the detection accuracy of the entire network is improved by optimizing the loss function. Moreover, increase weight ratio on wear particle in images by adding a convolution block CBAM model and increase feature representative capability in detection network with YOLOv5 algorithm detection network, which can improve detection accuracy for wear particle. Finally, compare the algorithm with the three classical homologous series object detection algorithm. The experimental results show that the detection accuracy of the model can reach 96.7%, and the detection speed is 32 FPS for the images with a resolution of $1280\times 720$ . It can be developed and applied to the fault diagnosis and condition monitoring of mechanical equipment.